Battery Health Prediction for Electric Vehicles Using Machine Learning

Battery Health Prediction for Electric Vehicles Using Machine Learning explores advanced techniques for monitoring and predicting the health of electric vehicle (EV) batteries. As EV adoption accelerates worldwide, accurate battery health estimation becomes critical for performance, safety, and cost optimization. This book presents a comprehensive study of data-driven approaches, including machine learning algorithms such as regression models, neural networks, and deep learning techniques. It covers battery degradation mechanisms, state-of-health (SOH) estimation, and predictive maintenance strategies using real-world datasets. Designed for researchers, engineers, and students, the book bridges the gap between electrical engineering and artificial intelligence, offering practical insights and implementation strategies. It serves as a valuable resource for developing intelligent battery management systems and advancing sustainable transportation technologies.

Krishnareddy Kuppireddy has been working as an Associate Professor in the Department of EEE at Mother Theresa Institute of Engineering and Technology, Palamaner, affiliated with Jawaharlal Nehru Technological University Ananthapuramu, Ananthapuramu, Andhra Pradesh, India. He completed his Bachelor of Technology (B. Tech) from JNTU Hyderabad. He completed a Master of Technology (M. Tech) (DSCE) from JNTUCEH, JNTU Hyderabad. He is pursuing a Doctor of Philosophy (Ph.D.) in Dept of CSE at KL University, Vijayawada, India. He published two patents in design and innovation. He also wrote 1 Textbook and 1 e-book available in Worldwide Best Bookseller Stores. He published 15 + Research papers in various reputed (Scopus/ SCI / Web of Science/ UGC approved). He acted as Program Chair and Editor for International Conference of the 1st CSEAi 2023 and acted as Session Chair of ICMLBDA 2022/23 and co-ordinator of ICCM 2022/23. He was honoured with the “Outstanding Researcher Award 2022”,” Young Scientist Award 2020”, and “Outstanding Scientist Award 2020” from I2OR International research organization. He has more than 15+ years of experience in teaching and research. Also, He has good knowledge of IoT and acquired GATE AIR 807 in 2007.